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Battery investment by a strategic wind producer: A scenario-based decomposition approach

机译：战略风力生产者的电池投资：一种基于场景的分解方法

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摘要

This paper develops an investment decision-making tool for a strategic wind producer, who desires to expand its portfolio by investing in battery systems. This enables the wind producer to better manage its production profile, which eventually yields an increased profit. The long-run decisions are strategic siting, sizing, and depth of discharge tuning of the battery system, and the short-run decisions are strategic offers to the market in terms of price and quantity. In this setup, we linearly model the technical characteristics of battery systems, e.g., depth of discharge, and then evaluate its impacts on the battery systems' number of cycles. The wind power uncertainty is modeled by a set of scenarios. The resulting model is a stochastic bi-level problem, which can be recast as a stochastic mixed-integer and linear model. However, this problem is generally hard-to-solve or even computationally intractable if many scenarios are considered. Hence, we use a scenario-based decomposition technique via progressive hedging algorithm to make the model scalable. An upper bound is then derived as a benchmark to assess the quality of results. Two case studies based on a six-bus and the IEEE 24-bus reliability test systems are used to evaluate the performance of the proposed approach.
机译：本文开发了一个战略风力生产商的投资决策工具，他们希望通过投资电池系统来扩展其投资组合。这使风力生产商能够更好地管理其生产型材，最终产生增加的利润。长期决策是电池系统的战略选址，尺寸和深度放电调整，短期决策是在价格和数量方面对市场的战略优惠。在该设置中，我们线性地模拟了电池系统的技术特性，例如放电深度，然后评估其对电池系统的循环次数的影响。风电不确定性由一组方案进行建模。得到的模型是随机双级问题，其可以作为随机混合整数和线性模型重新循环。但是，如果考虑了许多情况，这个问题通常是难以解决的甚至计算地棘手。因此，我们通过逐行对冲算法使用基于方案的分解技术，使模型可扩展。然后派生一个上限作为评估结果质量的基准。基于六公交车和IEEE 24公交车可靠性测试系统的两种案例研究用于评估所提出的方法的性能。

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